AI-driven data governance in banking: Leveraging large language models for compliance and risk management

Kamisetty, Rajesh and Nagamangalam, Raj (2025) AI-driven data governance in banking: Leveraging large language models for compliance and risk management. World Journal of Advanced Research and Reviews, 25 (3). pp. 1161-1169. ISSN 2581-9615

The banking sector has to deal with governance, compliance, and risk management challenges due to the evolving nature of the financial regulation and high volume of sensitive data. Real-time monitoring and anomaly detection are challenging in traditional rule based systems, which lead to inefficiencies and compliance risks. Using Large Language Models (LLMs), this paper discusses enabling banking data governance by automating compliance with banking regulations, risk assessment and fraud detection. Allow Intelligent data classification, predictive analytics and real-time auditing, in compliance with GDPR, Basel III, AML directive standards, etc. LLMs offer a transformative solution for secure and transparent financial operations, albeit with challenges like data privacy, model bias, explainability, etc. This research is based on real case studies and discusses how AI-based data governance can provide banks with improved security, compliance with regulatory mandates, and operational effectiveness